Verification of color vegetation indices for automated crop imaging applications

Meyer, George E.; Neto, J. Camargo

doi:10.1016/j.compag.2008.03.009

Cited by 673 publications

(347 citation statements)

References 22 publications

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“…The results shown so far are based on a VI that is calculated in a similar manner as the NDVI but using information from the blue and green spectral range. Visible light VIs, such as the NGRDI, are often used to characterize vegetation if NIR information is lacking (Pérez et al 2000; Meyer and Neto 2008; Raymond et al 2005). Due to their low costs and low weight, consumer-grade true colour (RGB) digital cameras are particularly suitable for assessing green vegetation using UAS-based imaging systems (Torres-Sánchez et al 2014; Saberioon et al 2014; Hoffmann et al 2016a; Goodbody et al 2017; Jannoura et al 2015).…”

Section: Resultsmentioning

confidence: 99%

Estimation of evapotranspiration of temperate grassland based on high-resolution thermal and visible range imagery from unmanned aerial systems

Brenner

Zeeman

Bernhardt

et al. 2018

International Journal of Remote Sensing

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Spatially distributed high-resolution data of land surface temperature (LST) and evapotranspiration (ET) are important information for crop water management and other applications in the agricultural sector. While satellite data can provide LST high-resolution data of 100 m, the current development of unmanned aerial systems (UAS) and affordable low-weight thermal cameras allows LST and subsequent ET to be derived at resolutions down to centimetre scale.In this study, UAS-based images in the thermal infrared (TIR) and visible spectral range were collected over a managed temperate grassland in July 2016 at the Terrestrial Environmental Observatories Networks TERENO preAlpine observatory site at Fendt, Germany. The UAS set-up included a lightweight thermal camera (Optris Pi Lightweight) and a regular digital camera (Sony α 6000) that allowed for the acquisition of thermal and optical images with a ground resolution of 5 cm and 1 cm, respectively. Three TIR-based ET models of different complexity were applied and the resulting ET estimates were compared to the Eddy covariance (EC) observations of turbulent energy fluxes and also to the evaporative fraction. While the Deriving Atmosphere Turbulent Transport Useful To Dummies Using Temperature (DATTUTDUT) model and the Triangle Method belong to the group of simpler contextual models, the Two-Source Energy Balance (TSEB) model incorporates a more physically based formulation of the surface energy balance. In addition to the comparison of UAS-based estimates of latent heat fluxes to EC observations, the effect of the spatial resolution of the model imagery input on the modelled results was analysed by running the models with imagery from the native resolution of the acquired images to resolutions that were aggregated up to 30 m.The results show that both contextual models are sensitive to the input image resolution and that the agreement with the EC observations improves with increasing image resolution. The TSEB model assumes that LST pixels represent a mixed signal of the soil and canopy components, thus an image resolution coarse enough to ensure this assumption should be chosen. However, with the exception of the native image resolution of 5 cm, we found no effect of image resolution on the spatially weighted mean TSEB estimates.For the studied grassland, the comparison of model estimates with EC observations indicates that all three models are able to reproduce observed energy fluxes with comparable accuracy with mean absolute errors of ET between 20 and 40 W m−2. The TSEB model showed larger deviations from the reference observations under cloudy conditions with rapid fluctuations of LST within the 30 min averaging period for EC. The two contextual models yielded similar results for most of the flights. The good performance of the DATTUTDUT model, which had the lowest input requirements of the three models, is especially promising in view of the application of UAS for routine near-real-time ET monitoring.

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Section: Resultsmentioning

confidence: 99%

Estimation of evapotranspiration of temperate grassland based on high-resolution thermal and visible range imagery from unmanned aerial systems

Brenner

Zeeman

Bernhardt

et al. 2018

International Journal of Remote Sensing

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“…The green index filter removes those points with a green index above the threshold selected by the user, for instance, green parts of vegetation standing at the banks or gully bottom. The green index is calculated using GI = 2g − b − r, where r, b and g stand for the pixel value of each of the colour bands in the RGB image (Meyer and Neto, 2008). The density filter is intended to remove those points with a low point density in their neighbourhood, typically related to lower accuracies.…”

Section: Processing Methodsology In Sf3mmentioning

confidence: 99%

SF3M software: 3-D photo-reconstruction for non-expert users and its application to a gully network

Castillo

James

Redel-Macías

et al. 2015

SOIL

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Abstract. Three-dimensional photo-reconstruction (PR) techniques have been successfully used to produce high-resolution surface models for different applications and over different spatial scales. However, innovative approaches are required to overcome some limitations that this technique may present for field image acquisition in challenging scene geometries. Here, we evaluate SF3M, a new graphical user interface for implementing a complete PR workflow based on freely available software (including external calls to VisualSFM and CloudCompare), in combination with a low-cost survey design for the reconstruction of a several-hundred-metres-long gully network. SF3M provided a semi-automated workflow for 3-D reconstruction requiring ∼ 49 h (of which only 17 % required operator assistance) for obtaining a final gully network model of > 17 million points over a gully plan area of 4230 m 2 . We show that a walking itinerary along the gully perimeter using two lightweight automatic cameras (1 s time-lapse mode) and a 6 m long pole is an efficient method for 3-D monitoring of gullies, at a low cost (∼ EUR 1000 budget for the field equipment) and the time requirements (∼ 90 min for image collection). A mean error of 6.9 cm at the ground control points was found, mainly due to model deformations derived from the linear geometry of the gully and residual errors in camera calibration. The straightforward image collection and processing approach can be of great benefit for non-expert users working on gully erosion assessment.

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“…Woebbecke et al (1995) determined that an excess green index was most effective, and it has been used widely since (e.g. Meyer and Neto 2008). Recently, Richardson et al (2007) developed a phenology monitoring program using a digital RGB web camera, and employed a measure similar to the excess green index referred to as 2G-RBi:…”

Section: Image Analysismentioning

confidence: 99%

Using digital time-lapse cameras to monitor species-specific understorey and overstorey phenology in support of wildlife habitat assessment

Bater

Coops

Wulder

et al. 2010

Environ Monit Assess

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Critical to habitat management is the understanding of not only the location of animal food resources, but also the timing of their availability. Grizzly bear (Ursus arctos) diets, for example, shift seasonally as different vegetation species enter key phenological phases. In this paper, we describe the use of a network of seven ground-based digital camera systems to monitor understorey and overstorey vegetation within species-specific regions of interest. Established across an elevation gradient in western Alberta, Canada, the cameras collected true-colour (RGB) images daily from 13 April 2009 to 27 October 2009. Fourth-order polynomials were fit to an RGB-derived index, which was then compared to field-based observations of phenological phases. Using linear regression to statistically relate the camera and field data, results indicated that 61% (r 2 = 0.61, df = 1, F = 14.3, p = 0.0043) of the variance observed in the field phenological phase data is captured by the cameras for the start of the growing season and 72% (r 2 = 0.72, df = 1, F = 23.09, p = 0.0009) of the variance in length of growing season. Based on the linear regression models, the mean absolute differences in residuals between predicted and observed start of growing season and length of growing season were 4 and 6 days, respectively. This work extends upon previous research by demonstrating that specific understorey and overstorey species can be targeted for phenological monitoring in a forested environment, using readily available digital camera technology and RGB-based vegetation indices.

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Verification of color vegetation indices for automated crop imaging applications

Cited by 673 publications

References 22 publications

Estimation of evapotranspiration of temperate grassland based on high-resolution thermal and visible range imagery from unmanned aerial systems

Estimation of evapotranspiration of temperate grassland based on high-resolution thermal and visible range imagery from unmanned aerial systems

SF3M software: 3-D photo-reconstruction for non-expert users and its application to a gully network

Using digital time-lapse cameras to monitor species-specific understorey and overstorey phenology in support of wildlife habitat assessment

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